Systems and methods for minimizing response variability of spinal cord stimulation

ABSTRACT

This present disclosure provides systems and methods relating to neuromodulation. In particular, the present disclosure provides systems and methods for identifying optimized temporal patterns of spinal cord simulation (SCS) for minimizing variability in patient responses to SCS. The systems and methods of neuromodulation disclosed herein facilitate the treatment of neuropathic pain associated with various disease states and clinical indications.

RELATED APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalPatent Application No. 62/597,606 filed Dec. 12, 2017, which isincorporated herein by reference in its entirety for all purposes.

FIELD

This present disclosure provides systems and methods relating toneuromodulation. In particular, the present disclosure provides systemsand methods for identifying optimized temporal patterns of spinal cordsimulation (SCS) for minimizing variability in patient responses to SCS.The systems and methods of neuromodulation disclosed herein facilitatethe treatment of neuropathic pain associated with various disease statesand clinical indications.

BACKGROUND

Spinal cord stimulation (SCS) involves the therapeutic application ofelectrical pulses using a surgically implanted device to treat chronicpain. The device is typically implanted near a targeted area of thespinal cord. Unfortunately, patient responses to SCS-based therapy arehighly variable and depend on a number of factors, including the anatomyof the spine and spinal cord, electrode position, and the origin,location and characteristics of pain.

These factors, among others, contribute to the variance in patientresponses to SCS (e.g., degree of pain relief). Changing the amplitude,pulse duration, and timing of the applied electrical pulses modulatesthe effectiveness of SCS in relieving pain, but the breadth of thisparameter space makes optimizing programming for specific patients asignificant clinical challenge. Furthermore, evaluating SCS performanceis difficult because patients have a broad range of parameter settingsand different device programmers use different protocols to selectparameters. Additionally, the location and characteristics of pain aswell as the effectiveness of any particular set of stimulationparameters vary over time, and these factors also contribute to thevariance of patient responses to SCS.

SUMMARY

Embodiments of the present disclosure include a method of identifying anoptimized spinal cord simulation (SCS) pattern for pain reduction. Inaccordance with these embodiments, the method includes generating aplurality of SCS patterns using an optimization algorithm based onpredetermined performance criteria, evaluating the plurality of SCSpatterns for pain reduction using a computational model of a neuronalnetwork, and identifying at least one candidate SCS pattern having anoptimized temporal pattern of stimulation capable of reducing pain.

In some embodiments, the plurality of SCS patterns are generated for atleast one of efficiency optimization, efficacy optimization, andvariance optimization. In some embodiments, the optimized SCS patternreduces pain in a plurality of subjects with different pain states. Insome embodiments, the optimized SCS pattern comprises a temporal patternof electrical stimulation pulses.

In some embodiments, the temporal pattern of electrical stimulationpulses comprises a non-regular temporal pattern with one or more varyinginter-pulse intervals. In some embodiments, the optimized SCS patterncomprises a stimulation frequency ranging from about 1 Hz to about 200Hz. In some embodiments, the optimization algorithm comprises at leastone of a genetic algorithm, a particle swarm algorithm, a simulatedannealing algorithm, an ant colony algorithm, an estimation ofdistribution algorithm, a gradient descent algorithm, and anycombinations and derivations thereof.

In some embodiments, the predetermined performance criteria areincorporated into a fitness function used to evaluate the fitness of theplurality of SCS patterns. In some embodiments, the predeterminedperformance criteria comprise at least one of: i) reduction in painscore; ii) SCS pattern efficiency; and iii) variance of pain scorereduction across different pain states.

In some embodiments, the reduction in the pain score comprises a changein firing rate and/or firing pattern of one or more neurons in thecomputational model. In some embodiments, the SCS pattern efficiency isproportional to the average frequency of stimulation. In someembodiments, the variance of pain score reduction across different painstates corresponds to variance of the response to SCS across apopulation of computational models of a neuronal network. In someembodiments, the response to SCS comprises a change in firing rateand/or firing pattern of one or more neurons in the computational model.In some embodiments, the computational model of the neuronal network iscoupled to the optimization algorithm by the predetermined performancecriteria. In some embodiments, the computational model of the neuronalnetwork simulates activity of a wide dynamic range (WDR) neuron. In someembodiments, the activity of the WDR neuron in the computational modelis a proxy for pain. In some embodiments, the computational model of theneural network comprises three network zones comprising heterogeneousinhibitory and excitatory neural connections. In some embodiments, thecomputational model of the neural network simulates a network state byvarying at least one of: a biophysical input parameter, a stimulationinput parameter, and a mechanism input parameter.

In some embodiments, the computational model simulates a pain state of asubject by varying the at least one biophysical input parameter, andwherein the at least one biophysical input parameter comprises: i)reversal potential of inhibitory synapses within each network zone; ii)maximum conductance of GABAergic synapses within each network zone; iii)maximum conductance of AMPA synapses onto inhibitory neurons within eachnetwork zone; and iv) number of C fibers activated in each surroundzone.

In some embodiments, the reversal potential of inhibitory synapsesranges from about −50 mV to about −100 mV. In some embodiments, themaximum conductance of GABAergic synapses ranges from about 50% to about100%. In some embodiments, the maximum conductance of AMPA synapses ontoinhibitory neurons ranges from about 50% to about 100%. In someembodiments, the number of C fibers activated in each surround zoneranges from about 0% to about 50%. In some embodiments, thecomputational model simulates a response to an SCS pattern by varyingthe at least one stimulation input parameter, and wherein the at leastone stimulation input parameter comprises: i) number of fibers activatedwithin each network zone by an SCS pattern; and ii) stimulationfrequency of an SCS pattern within each network zone. In someembodiments, the number of fibers activated within each network zone bythe SCS pattern ranges from about 0% to about 100% in a first networkzone, from about 0% to about 100% in a second network zone, and fromabout 0% to about 100% in a third network zone. In some embodiments, thestimulation frequency within each network zone ranges from about 1 Hz toabout 200 Hz.

In some embodiments, the computational model simulates a response to anSCS pattern by varying the at least one mechanism input parameter, andwherein the at least one mechanism input parameter comprises: i) maximumsodium conductance; and ii) maximum potassium conductance. In someembodiments, the maximum sodium conductance ranges from about 50% toabout 150% within a network zone. In some embodiments, the maximumpotassium conductance ranges from about 50% to about 150% within anetwork zone.

Embodiments of the present disclosure also include a system fordelivering spinal cord stimulation (SCS) to reduce pain. In accordancewith these embodiments, the system includes an electrode sized andconfigured for implantation in proximity to neural tissue, and a pulsegenerator coupled to the electrode. In some embodiments, the pulsegenerator includes a power source comprising a battery and amicroprocessor coupled to the battery. In some embodiments, the pulsegenerator is configured to generate electrical signals for delivering anSCS pattern having an optimized temporal pattern of electricalstimulation capable of reducing pain.

In some embodiments, the optimized SCS pattern reduces pain in aplurality of subjects with different pain states. In some embodiments,the optimized SCS pattern comprises non-regular temporal patterns withone or more varying inter-pulse intervals. In some embodiments,delivering an SCS pattern having an optimized temporal pattern ofelectrical stimulation comprises delivering one or more SCS patterns toone or more electrodes.

Embodiments of the present disclosure also include a method fordelivering spinal cord stimulation (SCS) to reduce pain using thesystems described above. In accordance with these embodiments, themethod includes programming the pulse generator to output the optimizedSCS pattern, and delivering the SCS pattern to a subject to reduce pain.

In some embodiments, the optimized SCS pattern reduces pain in aplurality of subjects with different pain states. In some embodiments,the optimized SCS pattern comprises non-regular temporal patterns withone or more varying inter-pulse intervals. In some embodiments,delivering an optimized SCS pattern to a subject comprises deliveringone or more different SCS patterns to one or more different neuronalpopulations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 includes representative results of patient pain rating scoresduring SCS treatment showing a significant degree of variability(Mean+SEM; n=5).

FIGS. 2A-2D depicts representative architecture of a distributedbiophysical network model of the dorsal horn and model responses tospinal cord stimulation. FIG. 2A: Synaptic connections for each neuronin the model are shown. FIG. 2B: Network architecture within the modelfor a single node. Primary afferents (Aβ, Aδ, and C) transmit inputs tolocal inhibitory (IN) and excitatory (EX) interneurons and a widedynamic range (WDR) projections neuron. SCS is represented as a constantfrequency input to the dorsal columns that moves antidromically alongthe Aβ fibers. The output of the node is the firing rate of the WDRneuron. FIG. 2C: Distributed multi-nodal model architecture. Each circlerepresents a node of the model from FIG. 2B with unique inputs. Thecenter node (Zone 1) receives excitatory inputs from zone 2 nodes andinhibitory inputs from both Zone 2 and Zone 3 nodes. All connectionsbetween nodes are from interneurons to WDR neurons. Connectionsrepresented in the model are bidirectional (e.g., Zone 2 excitatoryinterneurons project to the Zone 1 WDR neuron and the Zone 1 excitatoryintemeuron projects back to Zone 2 WDR neurons). The model is extendedto eliminate edge effects for the center node. FIG. 2D: Representationof receptive field with multiple zones on a rat foot.

FIG. 2E: Default model inputs and outputs in neuropathic pain conditionfor Zone 1 model neurons. Example spike trains for primary afferentfibers (15 Aβ, 15 Aδ, and 30 C) carrying ectopic activity to the centerneurons. One third of Aβ and Aδ fibers exhibit bursting behavior (bottomspike trains). The voltage traces of the output neurons are shown on theright. Scale bar, 100 ms and 30 mV.

FIG. 2F: Response of WDR neurons in each Zone from 1-150 Hz for thedefault network state. Traces represent the average response over 10trials. The shaded region represents the average minimum and maximumresponse for Zone 2 and Zone 3 across 10 trials. Left, raw modeled WDRresponses. Right, modeled WDR responses normalized to the baselinefiring rate in each simulation.

FIG. 3 includes a representative illustration of the biological basisfor the biophysical model of the dorsal horn network provided herein.

FIG. 4 includes representative Monte Carlo simulations used to determinethe effect of individual parameters on the response of model neurons toSCS. FIG. 4A: Inputs to the model that were varied in simulations.Stimulation parameters include the number of fibers activated by SCS andstimulation frequency in each zone. Mechanism parameters include thesodium and potassium conductance. Biophysical parameters include thereversal potential of the inhibitory synapses, maximum conductance ofGABAergic synapses, maximum conductance of AMPA synapses onto INinterneurons, and the number of C fibers activated in surround Zones.Grey represents the actual parameter that is changing, and the numberscorrespond to parameter numbers in FIG. 4D. (See Table 1 for a moredetailed description of each parameter.) FIG. 4B: Demonstrates thatchanges in the input parameters generated 2000 different network states.FIG. 4C: Representative responses of 500 network states versusstimulation input frequencies between 1 and 150 Hz. All responses arenormalized to the baseline response with no SCS input. FIG. 4D:Quantifies the effect size of various parameters on the variance of thefiring rate of the WDR neuron in zone 1 using η². The proportion of thebar represents the magnitude of the effect of each parameter across allsimulations and at particular frequencies of interest. The parametersare sorted by color according to Table 1. The analysis is repeated(labeled Δ E_(rev)=0 mV) excluding model states with changing reversalpotential because the effect of reversal potential dominated at somefrequencies.

FIG. 5 includes a representative box plot of the range of outputsgenerated from changing the inputs of the model. Red lines indicatemedians, box edges demarcate the 25^(th) and 75^(th) percentiles, dottedline edges indicate +/−2.7σ for a normally distributed data set, and redcrosses indicate outliers.

FIG. 6 includes a representative method for producing a temporallyoptimized SCS pattern using a genetic algorithm. Temporal patterns ofstimulation are the inputs to the model. The algorithm evaluates thefitness of the pattern based on how much they reduce pain in the model,the efficiency of the pattern, and the reduction in variance of the painscore across multiple trials. The pain score in the model isproportional to the firing rates of the WDR neurons. The algorithmdesigns new patterns of stimulation based on the patters with thehighest fitness from the previous round.

FIGS. 7A-7C include a representative genetic algorithm run acrossmultiple network states (FIG. 7A), and a representative graph showingthe range of performance scores across network states for the besttemporal pattern of SCS as the algorithm evolves (FIG. 7B). Decreasingscores as indicated by the placement of the bar along the y-axis in theplot represent increases in the efficacy of the patterns whilereductions in the range of scores decreases the variance in efficacyacross states. FIG. 7C: Examples of the top five performing 1000 ms longtemporal patterns of SCS evolving between the 1^(st) and 25^(th)generation of the genetic algorithm.

FIGS. 8A-8C include a representative genetic algorithm run acrossmultiple network states (FIG. 8A) with a different scoring function fromthe run shown in FIGS. 7A-7C. The changes are due to changing therelative weights of the components in the scoring function (FIG. 6).FIG. 8B: A representative graph showing the range of performance scoresacross network states for the best temporal pattern of SCS as thealgorithm evolves. Decreasing scores as indicated by the placement ofthe bar along the y-axis in the plot represent increases in the efficacyof the patterns while reductions in the range of scores decreases thevariance in efficacy across states. FIG. 8C: Examples of the top fiveperforming 1000 ms long temporal patterns of SCS evolving between the1^(st) and 50^(th) generation of the genetic algorithm.

DETAILED DESCRIPTION

Section headings as used in this section and the entire disclosureherein are merely for organizational purposes and are not intended to belimiting.

1. DEFINITIONS

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art. In case of conflict, the present document, includingdefinitions, will control. Preferred methods and materials are describedbelow, although methods and materials similar or equivalent to thosedescribed herein can be used in practice or testing of the presentdisclosure. All publications, patent applications, patents and otherreferences mentioned herein are incorporated by reference in theirentirety. The materials, methods, and examples disclosed herein areillustrative only and not intended to be limiting.

The terms “comprise(s),” “include(s),” “having,” “has,” “can,”“contain(s),” and variants thereof, as used herein, are intended to beopen-ended transitional phrases, terms, or words that do not precludethe possibility of additional acts or structures. The singular forms“a,” “and” and “the” include plural references unless the contextclearly dictates otherwise. The present disclosure also contemplatesother embodiments “comprising,” “consisting of” and “consistingessentially of,” the embodiments or elements presented herein, whetherexplicitly set forth or not.

For the recitation of numeric ranges herein, each intervening numberthere between with the same degree of precision is explicitlycontemplated. For example, for the range of 6-9, the numbers 7 and 8 arecontemplated in addition to 6 and 9, and for the range 6.0-7.0, thenumber 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 areexplicitly contemplated. Recitation of ranges of values herein aremerely intended to serve as a shorthand method of referring individuallyto each separate value falling within the range, unlessotherwise-Indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. Forexample, if a concentration range is stated as 1% to 50%, it is intendedthat values such as 2% to 40%, 10% to 30%, or 1% to 3%, etc., areexpressly enumerated in this specification. These are only examples ofwhat is specifically intended, and all possible combinations ofnumerical values between and including the lowest value and the highestvalue enumerated are to be considered to be expressly stated in thisdisclosure.

“Pain” generally refers to the basic bodily sensation induced by anoxious stimulus, received by naked nerve endings, characterized byphysical discomfort (e.g., pricking, throbbing, aching, etc.) andtypically leading to an evasive action by the individual. As usedherein, the term pain also includes chronic and acute neuropathic pain.The term “chronic neuropathic pain” refers to a complex, chronic painstate that is usually accompanied by tissue injury wherein the nervefibers themselves may be damaged, dysfunctional or injured. Thesedamaged nerve fibers send incorrect signals to other pain centers. Theimpact of nerve fiber injury includes a change in nerve function both atthe site of injury and areas around the injury. The term “acuteneuropathic pain” refers to self-limiting pain that serves a protectivebiological function by acting as a warning of on-going tissue damage.Acute neuropathic pain is typically a symptom of a disease processexperienced in or around the injured or diseased tissue.

“Subject” and “patient” as used herein interchangeably refers to anyvertebrate, including, but not limited to, a mammal (e.g., cow, pig,camel, llama, horse, goat, rabbit, sheep, hamsters, guinea pig, cat,dog, rat, and mouse, a non-human primate (e.g., a monkey, such as acynomolgus or rhesus monkey, chimpanzee, etc.) and a human). In someembodiments, the subject may be a human or a non-human. In oneembodiment, the subject is a human. The subject or patient may beundergoing various forms of treatment.

“Treat,” “treating” or “treatment” are each used interchangeably hereinto describe reversing, alleviating, or inhibiting the progress of adisease and/or injury, or one or more symptoms of such disease, to whichsuch term applies. Depending on the condition of the subject, the termalso refers to preventing a disease, and includes preventing the onsetof a disease, or preventing the symptoms associated with a disease. Atreatment may be either performed in an acute or chronic way. The termalso refers to reducing the severity of a disease or symptoms associatedwith such disease prior to affliction with the disease. Such preventionor reduction of the severity of a disease prior to affliction refers toadministration of a treatment to a subject that is not at the time ofadministration afflicted with the disease. “Preventing” also refers topreventing the recurrence of a disease or of one or more symptomsassociated with such disease.

“Therapy” and/or “therapy regimen” generally refer to the clinicalintervention made in response to a disease, disorder or physiologicalcondition manifested by a patient or to which a patient may besusceptible. The aim of treatment includes the alleviation or preventionof symptoms, slowing or stopping the progression or worsening of adisease, disorder, or condition and/or the remission of the disease,disorder or condition. In some embodiments, the treatment comprises thetreatment, alleviation, and/or lessening of pain.

Unless otherwise defined herein, scientific and technical terms used inconnection with the present disclosure shall have the meanings that arecommonly understood by those of ordinary skill in the art. For example,any nomenclatures used in connection with, and techniques of, cell andtissue culture, molecular biology, neurobiology, microbiology, genetics,electrical stimulation, neural stimulation, neural modulation, andneural prosthesis described herein are those that are well known andcommonly used in the art. The meaning and scope of the terms should beclear; in the event, however of any latent ambiguity, definitionsprovided herein take precedent over any dictionary or extrinsicdefinition. Further, unless otherwise required by context, singularterms shall include pluralities and plural terms shall include thesingular.

2. OPTIMIZED SCS PATTERNS

Embodiments of the present disclosure provide methods for designingoptimized temporal patterns of stimulation to minimize the variance ofpatient responses to SCS (see, e.g., FIG. 1). This variance includes,for example, patient-to-patient response variability, as well asvariance over time within a particular patient. In some embodiments, themethods include the use of a validated computational model of SCS todesign optimized patterns of SCS using a fitness function to evaluatethe fitness of the SCS patterns. This design method and the resultingtemporal patterns of stimulation will provide various advantages,including but not limited to, improved clinical efficacy and simplifiedneuromodulation device programming.

In some embodiments, the methods include the use of a computationalmodel of the effects of SCS on spinal cord pain networks. For example,computational models indicated that the activity of wide dynamic range(WDR) neurons that transmit pain signals to the brain is stronglydependent on the pulse repetition frequency of SCS (see, e.g., FIG. 2).The firing rate of model WDR neurons is generally considered a validatedproxy for the level of pain (e.g., neuropathic pain), as there is asignificant correlation between firing rate of model WDR neurons andpain ratings, and changes in WDR firing rates during SCS parallelbehavioral effects on pain.

Network changes in the spinal cord, such as a reduction of inhibition,changes in intrinsic plasticity, and abnormal functioning of afferentsinfluence pain sensations. The computational models provided hereinincorporate representations of these network changes to representvariability in pain state. For example, loss of function of KCC2 Cl⁻transporters can lead to a depolarizing shift in anionic reversalpotentials in the local network. Additionally, loss of GABAergicinhibition decreases the weight of afferent inputs onto inhibitoryinterneurons and from inhibitory interneurons to WDR neurons in localand surround networks. And spreading pain increases the number of smalldiameter (C-fiber) afferents with ectopic activity in surround networks.

In accordance with embodiments of the present disclosure, variance inpain states were simulated (within and across patients) by changing theweighted input levels of the model variants across a large number ofsimulations (see, e.g., FIGS. 4A-4D). This can be accomplished, forexample, with Latin hypercube Monte Carlo sampling or Design ofExperiments approaches, or other suitable methods. Applying differentSCS frequencies to different modeled pain states indicated that there issignificant variability in the model output, and it is this variabilityacross the changes in model parameters that represents the pain variancewithin and across patients. Furthermore, this variability is at leastpartially dependent on the stimulation frequency, indicating thatchanging stimulation parameters can be a useful way of determining theamount of variability in patient responses.

The systems and methods provided herein introduce novel means fordesigning and evaluating optimized temporal patterns of SCS to reducethe variability in response to SCS across pain states within a patientand between patients, and in a plurality of patients with different painstates, which increases clinical efficacy and simplifies neuromodulationdevice programming. In some cases, pain states can refer to the painexperienced by different patients with the same disease etiology (e.g.,source of neuropathic pain), and/or the pain experienced by differentpatients with different disease etiologies, and/or the pain experiencedby a single patient as his/her condition improves, worsens, or changes.The methods provided herein include the use of an optimization algorithm(e.g., genetic algorithm, particle swarm optimization, simulatedannealing) to design temporal patterns of stimulation to reduce thevariance of the changes in firing in a neuron in the computation model(see, e.g., FIG. 6). For example, a pattern could be designed to reducethe firing rate of the WDR neuron, and thereby reduce pain. The methodsfurther provide for the identification of temporal patterns to reducethe variance of the reduction of neural firing rate, such that theresulting pattern produces robust reductions in pain across diseaseconditions and clinical indications.

In some embodiments, generating a SCS pattern having an optimizedtemporal pattern of stimulation is performed using an optimizationalgorithm, such as a genetic algorithm. A genetic algorithm uses naturalselection as a basis for solving optimization problems. Although variousembodiments described herein are based on the use of a geneticalgorithm, other optimization algorithms may be employed in acomputational model of neural stimulation. Other optimization algorithmsthat may be used include, for example, simulated annealing, Monte-Carlomethods, other evolutionary algorithms (e.g., genetic algorithm,evolutionary programming, genetic programming), swarm algorithms (e.g.,ant colony optimization, bees optimization), differential evolution,firefly algorithm, invasive weed optimization, harmony search algorithm,and/or intelligent water drops. Additionally, as would be recognized byone of ordinary skill in the art based on the present disclosure, otheroptimization methods and algorithms can also be used in conjunction withthe methods and systems designed herein.

In accordance with these embodiments, the computation model receives atemporal pattern of stimulation as an input. The fitness of the temporalpattern was evaluated using a fitness function based on three factors:the reduction in pain score in the model (where the pain score isrelated to a change in firing rate and or pattern of one or more neuronsin the model), the efficiency of the pattern (where the efficiency isproportional to the average frequency of stimulation and is importantfor evaluating the impact on battery life or recharge interval ofimplantable pulse generations), and the variance of the reduction ofpain scores across different pain models (where the reduction invariance in model responses corresponds to a reduction of within oracross patient variability in the response to SCS). The geneticalgorithm designs new temporal patterns based on the patterns thatreceived the highest fitness scores in the previous round. The processrepeats until the algorithm has converged on an optimal or optimizedsolution.

In some embodiments, each term in the fitness function of theoptimization algorithm has a weighting coefficient, which allows for thecontrol of how much each of the factors will influence pattern design orselection. Optimization algorithm applications have previously usedfitness functions that optimize for performance and efficiency. However,embodiments of the present disclosure include methods that introducevariance reduction in pain states as a new term to the fitness functionto improve clinical efficacy.

In accordance with these embodiments, and as exemplified in FIGS. 7 and8, optimized SCS patterns can include a temporal pattern of electricalstimulation pulses, and/or the temporal pattern of electricalstimulation pulses can include a non-regular temporal pattern with oneor more varying inter-pulse intervals. In some embodiments, the patternof electrical stimulation may be applied at multiple differentfrequencies and at different timings. Further, the patterns may beapplied at different frequencies that are multiples of each other. Thepattern of electrical stimulation may include regular temporal patternsof stimulation (e.g., constant interpulse intervals) or non-regulartemporal patterns of stimulation (e.g., interpulse intervals that varyin time). In some embodiments, the optimized SCS pattern comprises astimulation frequency ranging from about 1 Hz to about 200 Hz, fromabout 1 Hz to about 175 Hz, from about 1 Hz to about 150 Hz, from about1 Hz to about 125 Hz, from about 1 Hz to about 100 Hz, from about 1 Hzto about 75 Hz, from about 1 Hz to about 50 Hz, from about 50 Hz toabout 150 Hz, from about 50 Hz to about 100 Hz, and from about 100 Hz toabout 200 Hz.

3. METHODS AND SYSTEMS

Embodiments of the present disclosure include a method of identifying anoptimized spinal cord simulation (SCS) pattern for pain reduction. Inaccordance with these embodiments, the method includes generating aplurality of SCS patterns using an optimization algorithm based onpredetermined performance criteria. In some embodiments, the pluralityof SCS patterns can be generated for at least one of efficiencyoptimization, efficacy optimization, and variance optimization.

In some embodiments, the optimization algorithm is a genetic algorithm,a particle swarm algorithm, a simulated annealing algorithm, an antcolony algorithm, an estimation of distribution algorithm, and anycombinations and derivations thereof (see, e.g., FIG. 6). In accordancewith these embodiments, the method can include an algorithm thatcontrols the delivery of multiple frequencies of SCS through differentoutput channels to different contacts on a SCS electrode (FIG. 2).

Evaluating the SCS patterns can occur using, for example, a geneticalgorithm in which optimal stimulation patterns are developed andevaluated over several iterations, or “generations.” In someembodiments, the genetic algorithm evaluates SCS patterns across about50 to about 150 generations. In some embodiments, the first generationincludes 25 randomly generated patterns, each containing about 1000“bits” representing 1 millisecond bins during which an SCS pulse may bedelivered over a given 1 second interval; the overall SCS pulse trainduring the 5-second stimulation period can be built from 5 successiverepeats of a given pattern. Optimization methods in accordance withthese embodiments can be used to design or identify unique temporalpatterns of SCS that are more effective at suppressing model WDR neuronbehavior versus equivalent regular frequency stimulation through testingof the prototype algorithm using a computational model of pain.

In some embodiments, the computational model of the neuronal network iscoupled to the optimization algorithm by the predetermined performancecriteria. For example, the algorithm may use the output of model WDRprojections neurons responsible for transmitting nociceptive informationto the brain to optimize the temporal pattern of stimulation deliveredduring SCS such that stimulation suppresses the activity of these WDRneurons as much as possible and at the lowest possible frequency. Insome embodiments, the computational model shown in FIG. 2E may beutilized. In some embodiments, the predetermined performance criteriacan be incorporated into a fitness function used to evaluate the fitnessof the plurality of SCS patterns. The predetermined performance criteriacan include at least one of a reduction in pain score (e.g., efficacy ofreducing WDR activity), SCS pattern efficiency (e.g., reducingstimulation frequency), and variance of pain score reduction acrossdifferent pain states. The relative significance of these performancecriteria can be controlled by modifying a weighting coefficient togenerate a family of temporally optimized stimulation patterns.

In some embodiments, the reduction in the pain score includes a changein firing rate and/or firing pattern of one or more neurons in thecomputational model. In some embodiments, the SCS pattern efficiency isproportional to the average frequency of stimulation. And in someembodiments, the variance of pain score reduction across different painstates corresponds to variance of the response to SCS across apopulation of computational models of a neuronal network. The responseto SCS can also include a change in firing rate and/or firing pattern ofone or more neurons in the computational model.

4. COMPUTATIONAL MODELS

Embodiments of the systems and methods of the present disclosure alsoinclude evaluating a plurality of SCS patterns for pain reduction usinga computational model of a neuronal network, such as a computationalnetwork model of the dorsal horn described above (see, e.g., FIG. 3). Insome embodiments, the methods include determining one or more of thenon-regular temporal patterns that results in predetermined WDR neuronaloutput and stimulation activity. Predetermined WDR neuronal output mayinclude, but is not limited to, the output of a model WDR neuron in asimulation implemented in a computational model, which has inputs formodeling a biological WDR neuron. In this sense, the model WDR neuron'soutput can be used as a proxy for patient pain (i.e. efficacy).

In one example, a computing device can be used to generate and utilize acost function for optimizing the WDR neuronal output and stimulationactivity. Further, the computing device can select one or more of thenon-regular temporal patterns based on the cost function. Further, thecomputing device can alter the temporal patterns and determine when athreshold value for the cost function is obtained while altering thetemporal patterns. Additionally, the computing device can determine thatthe temporal pattern applied when the threshold value is obtained is thenon-regular temporal pattern(s) that results in predetermined WDRneuronal output and stimulation activity. This temporal pattern may bedetermined to be the temporal pattern that provides the lowest WDRneuronal output, and/or the lowest stimulation activity, and/or thehighest variance reduction among all other applied temporal patterns. Asreferred to herein, the term “efficacy” refers to the minimization ofmodel WDR activity (proxy for reduced pain); the term “efficiency”refers to a low or the lowest possible device stimulation frequency(power savings); and the term “variance reduction” refers to variance inthe response to SCS across a population of computational models of aneuronal network.

In some embodiments, the computational model of the neuronal networksimulates activity of a WDR neuron, which is used as a proxy for pain.As shown in FIGS. 2A-2D, the computational model of the neural networkcan include three network zones comprising heterogeneous inhibitory andexcitatory neural connections. In accordance with these embodiments, thecomputational model of the neural network can simulate a network stateby varying at least one of: a biophysical input parameter, a stimulationinput parameter, and a mechanism input parameter.

In some embodiments, the biophysical input parameter can include: i)reversal potential of inhibitory synapses within each network zone; ii)maximum conductance of GABAergic synapses within each network zone; iii)maximum conductance of AMPA synapses onto inhibitory neurons within eachnetwork zone; and iv) number of C fibers activated in each surroundzone. The reverse potential of inhibitory synapses can range from about−50 mV to about −100 mV; the maximum conductance of GABAergic synapsescan range from about 50% to about 100%; the maximum conductance of AMPAsynapses onto inhibitory neurons can range from about 50% to about 100%;and the number of C fibers activated in each surround zone can rangefrom about 0% to about 50%.

In some embodiments, the computational model can simulate a response toan SCS pattern by varying the at least one stimulation input parameter.In accordance with these embodiments, the at least one stimulation inputparameter can include: i) number of fibers activated within each networkzone by an SCS pattern; and ii) stimulation frequency of an SCS patternwithin each network zone. The number of fibers activated within eachnetwork zone by the SCS pattern can range from about 0% to about 100% ina first network zone, from about 0% to about 100% in a second networkzone, and from about 0% to about 100% in a third network zone. Thestimulation frequency within each network zone can range from about 1 Hzto about 200 Hz, from about 1 Hz to about 175 Hz, from about 1 Hz toabout 150 Hz, from about 1 Hz to about 125 Hz, from about 1 Hz to about100 Hz, from about 1 Hz to about 75 Hz, from about 1 Hz to about 50 Hz,from about 50 Hz to about 150 Hz, from about 50 Hz to about 100 Hz, andfrom about 100 Hz to about 200 Hz.

In some embodiments, the computational model can simulate a response toan SCS pattern by varying the at least one mechanism input parameter. Inaccordance with these embodiments, the at least one mechanism inputparameter can include: i) maximum sodium conductance; and ii) maximumpotassium conductance. The maximum sodium conductance can range fromabout 50% to about 150% within a network zone, and the maximum potassiumconductance can range from about 50% to about 150% within a networkzone.

5. SCS DELIVERY SYSTEMS AND METHODS

Notwithstanding the embodiments described herein, the methods andsystems for administering SCS based on temporal patterns of stimulationdescribed in U.S. patent application Ser. Nos. 14/774,156, 14/774,160,and 15/806,686 are herein incorporated by reference in their entiretiesand for all purposes.

Additionally, SCS delivery systems and methods of the present disclosureinclude a system for delivering spinal cord stimulation to a subject inorder to reduce, treat, or prevent the subject's neuropathic pain. Inaccordance with these embodiments, the system includes an electrodesized and configured for implantation in proximity to neural tissue. Forexample, the system can include an SCS device, an electrical connectionlead, and at least one electrode or electrode array operativelypositioned in the epidural space of a vertebral column of a subject thatis experiencing neuropathic pain. The electrode or electrode array canbe positioned at the site of nerves that are the target of stimulation(e.g., along the spinal cord), or positioned in any suitable locationthat allows for the delivery of electrical stimulation to the targetedneural tissue.

In some embodiments, the system includes a pulse generator coupled tothe electrode. The pulse generator can include a power source comprisinga battery and a microprocessor coupled to the battery, and the pulsegenerator is generally configured to generate electrical signals fordelivering an SCS pattern having an optimized temporal pattern ofelectrical stimulation capable of reducing pain. In some embodiments,the system further includes a controller comprising hardware, software,firmware, or combinations thereof for implementing functionalitydescribed herein. For example, the controller can be implemented by oneor more processors and memory. The controller can be operativelyconnected to the pulse generator to facilitate the generation ofelectrical signals and applying temporal patterns of electricalstimulation to targeted neurological tissue. The output signals may bereceived by the connection lead and carried to the electrode orelectrode array for the delivery of electrical stimulation to targetedneurological tissue. The system can include a power source, such as abattery, for supplying power to the controller and the pulse generator.

Embodiments of the present also include methods for delivering spinalcord stimulation to reduce pain using the systems described herein. Inaccordance with these embodiments, the method includes programming thepulse generator to output the optimized SCS pattern and delivering theSCS pattern to a subject to reduce pain. In some embodiments, theoptimized SCS pattern reduces pain in a plurality of subjects withdifferent pain states. In some embodiments, the optimized SCS patternincludes non-regular temporal patterns with one or more varyinginter-pulse intervals. And in some embodiments, delivering an optimizedSCS pattern to a subject includes delivering one or more different SCSpatterns to one or more different neuronal populations.

In some embodiments, the system also includes an external computingdevice that is not implanted within the subject. The computing devicecan communicate with an SCS device or system via any suitablecommunication link (e.g., a wired, wireless, or optical communicationlink). The communication link may also facility battery recharge. Aclinician may interact with a user interface of the computing device forprogramming the output of the implanted pulse generator, including theelectrodes that are active, the stimulation pulse amplitude, thestimulation pulse duration, the stimulation pattern (including pulserepetition frequency), and the like applied via each electrode contactto each sub-population. In accordance with these embodiments, systemsand methods of the present disclosure can be used to deliver optimizedSCS patterns, as described herein, to reduce pain in a plurality ofsubjects with different pain states. The optimized SCS pattern caninclude non-regular temporal patterns with one or more varyinginter-pulse intervals. In some embodiments, delivering an SCS patternhaving an optimized temporal pattern of electrical stimulation includesdelivering one or more SCS patterns to one or more electrodes.

In some embodiments, systems and methods of the present disclosure canbe implemented as an algorithm within a SCS pulse generator device. Anon-board controller can deliver multiple frequencies and patterns of SCSthrough different output channels to different contacts on the spinalcord stimulation electrode. By virtue of stimulation through multiplecontacts, different populations of axons (e.g., sub-populations ofdorsal column nerve fibers) traversing the dorsal column may beactivated at different frequencies and in different patterns, resultingin greater suppression of the neurons responsible for transmittingnociceptive information to the brain. Values of the stimulationfrequencies and patterns of stimulation and the electrodes through whichthese frequencies and patterns are delivered can be input by either aphysician or a patient through a user interface. Alternatively, thedevice can be pre-programmed with specific combinations of frequenciesand patterns to use. The applied frequencies and patterns may or may notbe offset from each other at the start of stimulation. In addition, thedelivered frequencies and patterns of SCS may be limited to 2frequencies and patterns, as many frequencies and patterns and axonpopulations as the stimulation technology will allow can be delivered tothe patient. The algorithm can be toggled on and off (e.g., betweenmulti-frequency and single frequency SCS) by either the physician orpatient, or it can be coupled to an internal feedback-driven algorithmfor automatic control.

In some embodiments, computer readable program instructions for carryingout operations of the present disclosure, including programming thepulse generator to output the optimized SCS pattern, can be assemblerinstructions, instruction-set-architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, firmwareinstructions, state-setting data, or either source code or object codewritten in any combination of one or more programming languages,including an object oriented programming language such as Java,Smalltalk, C++ or the like, and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present subject matter.

6. EXAMPLES

It will be readily apparent to those skilled in the art that othersuitable modifications and adaptations of the methods of the presentdisclosure described herein are readily applicable and appreciable, andmay be made using suitable equivalents without departing from the scopeof the present disclosure or the aspects and embodiments disclosedherein. Having now described the present disclosure in detail, the samewill be more clearly understood by reference to the following examples,which are merely intended only to illustrate some aspects andembodiments of the disclosure, and should not be viewed as limiting tothe scope of the disclosure. The disclosures of all journal references,U.S. patents, and publications referred to herein are herebyincorporated by reference in their entireties.

The present disclosure has multiple aspects, illustrated by thefollowing non-limiting examples.

Example 1

Network Architecture and Biophysics.

A biophysically-based computational network model of the dorsal horn wasused as the basis for the distributed network model described herein.The model includes the use of primary afferent fibers that conveyedinformation from the peripheral receptive field to the model neuronsthat represented central sensory processing within the dorsal horn.Peripheral spatiotemporal inputs to the model were conveyed throughthree types of primary afferents: large diameter afferents (Aβ) conveytouch information, while smaller thinly myelinated (Aδ) and unmyelinated(C) afferents convey nociceptive information. The afferent fiberscommunicated information through spikes drawn from a homogeneous Poissonprocess representing biological spike rates with delays drawn fromconduction velocities provided in previous studies. SCS inputs wereapplied through Aβ fibers. Since SCS inputs are assumed to propagateantidromically from the stimulation site to the network, a 100-mmdistance between the stimulation site and the network was assumed, and aspike collision model between orthodromic peripheral inputs and SCSinputs was implemented. The network used three types of model neurons:inhibitory (IN) interneurons, excitatory (EX) interneurons, andwide-dynamic range (WDR) projection neurons. The synaptic connectionswithin the model were based on the previous model with somemodifications to represent the expanded network architecture, as shownin FIG. 2A.

The network architecture of the model was multilayered with individualnodes representing processing within different zones and inhibitoryand/or excitatory connections between nodes representing connectionsacross the entire receptive field. The connections within each node(FIG. 2B) were based on the gate control theory and computational modelsof the dorsal horn. Model IN interneurons received inputs from Aβfibers, EX interneurons received inputs from C fibers and the INinterneuron, and WDR projection neuron received inputs from Aβ, Aδ, andC fibers, as well as IN and EX interneurons. The connections betweenzones (FIG. 2C) were based on experimental recordings of surroundinhibition showing three distinct zones. Zone 1 receives informationfrom primary afferents in the center of the receptive field, zone 2 fromthe area immediately surrounding zone 1, and zone 3 from the peripheralareas of the receptive field (FIG. 2D). Zone 2 sends both inhibitory andhigh threshold excitatory inputs to zone 1. Inhibition from zone 2increases the focality for painful stimuli while the high-thresholdexcitatory inputs increase the magnitude of signals for larger stimuli.Zone 3 and sends only inhibitory inputs to zone 1.

Inhibitory connections between zones were from the surround INinterneuron to the local EX interneuron and local WDR projectionsneuron. Excitatory connections between zones were from the surround EXneuron to the local WDR projection neuron. As disclosed herein, surroundEX or IN neuron generally refers to an excitatory or inhibitory neuronthat receives peripheral inputs from the surround area of the center ofthe receptive field. Note that the connections were bidirectionalbecause the surround of the zone 1 receptive field area is the center ofanother receptive field area. Therefore zone 1 also sent inhibitoryconnections to zone 2 and zone 3 nodes. The weight of the connectionswas modified from the weight of surround inhibition, so that thebaseline firing rate of the zone 1 WDR neuron in the default networkstate did not change. The total conductance of inhibitory synapses fromlocal IN to local WDR was about 5.3 nS for both glycinergic andGABAergic connections. The total conductance of GABAergic inhibition wasabout 5.8 nS for surround IN to local WDR synapses and about 7.3 nS forsurround IN to local EX synapses. The total conductance of AMPAconnections was about 2.2 nS for local EX to local WDR synapses andabout 1.5 nS for surround EX to local WDR synapses.

The total conductance of synapses between primary afferents and modelneurons was the same as in previous studies (e.g., Zhang et al.(2014b)). The membrane dynamics of the individual neurons and modelneuron geometry were also unchanged. Briefly, neuron dynamics are basedon patch-clamp recordings from substantia gelatinosa and deep dorsalhorn neurons. Each neuron is a Hodgkin-Huxley type membrane model withfour segments—a dendrite, soma, axon initial segment (hillock), andaxon. The ionic currents in each component of the neuron and thecompartment sizes match previous studies. All simulations were conductedin the NEURON simulation environment (v7.4 and v7.5).

Example 2

Network Model Variation.

The Monte Carlo method relies on repeated random sampling of inputs tocompute results for uncertain scenarios. In the present disclosure,random network states were generated by varying several parameters inthree categories: stimulation, mechanisms, and biophysical parameters.Stimulation parameters represented possible changes to the SCS programthrough the spatial complement of fibers that was activated or bymodifying the frequency. Mechanism parameters represented randomdifferences in the conductances of projection neurons that changeexcitability. Biophysical parameters represented the changes in networkstates that occur due to progression of neuropathic pain and loss ofinhibition in the network. Table 1, below, includes a description ofeach parameter and the constraints used for the Monte Carlo simulations.

TABLE 1 Inputs for Monte Carlo simulations (constraints for theparameters representing pain in the model). Input Names Range (units)Description Stimulation Frequency 1, 10, 30, 50, 100, 150 (Hz) SCSstimulation frequency SCS_(Z1) 50 ↔ 100 (%) Proportion of Aβ fibersactivated in zone 1 by SCS input SCS_(Z2)  0 ↔ 100 (%) Proportion of Aβfibers activated in zone 2 by SCS input SCS_(Z3)  0 ↔ 100 (%) Proportionof Aβ fibers activated in zone 3 by SCS input Mechanism g_(Na) 50 ↔ 150(%) Maximum sodium conductance for zone 1 WDR neuron g_(K) 50 ↔ 150 (%)Maximum potassium conductance for zone 1 WDR neuron Biophysical E_(rev)−70, −66, −62, −58, −54 (mV) Shift in anionic reversal potentials due toloss of function of the Cl⁻ transporter KCC2 Aβ → IN_(Local) 50 ↔ 100(%) Change in conductance of Aβ fiber inputs to local inhibitoryinterneuron Aβ → IN_(Surround) 50 ↔ 100 (%) Change in conductance of Aβfiber inputs to surround inhibitory interneuron IN → WDR_(Local) 50 ↔100 (%) Change in GABAergic conductance from local inhibitoryinterneuron (IN) to local WDR neuron In → WDR_(Surround) 50 ↔ 100 (%)Change in GABAergic conductance from surround inhibitory interneurons(IN) to local WDR neuron Pain_(z2)  0 ↔ 50 (%) Activation of smalldiameter (Aδ & C) fibers in zone 2 Pain_(z3)  0 ↔ 50 (%) Activation ofsmall diameter (Aδ & C) fibers in zone 3

Random network states were generated with latin hypercube sampling (LHS)of input parameters (except frequency). LHS generates a square grid of Mintervals for N variables within the range for each parameter where M isthe number of trials. LHS ensures that the random sample is a sufficientrepresentation of the variability within the parameter. Each networkstate was tested at 1, 10, 30, 50, 100, and 150 Hz because thesefrequencies were identified as important inflection points.

Example 3

Genetic Algorithm.

This example provides a representative method of identifying anoptimized SCS pattern for pain reduction using a genetic algorithm.FIGS. 7A-7C show the performance of the best pattern across ninedifferent network states and twenty-five generations. In particular,FIG. 7A includes a representation of multiple network states, while FIG.7B includes a representative graph (boxplots) showing the range ofperformance scores across network states for the best temporal patternof SCS as the algorithm evolves. Decreasing scores as indicated by theplacement of the bar along the y-axis in the plot represent increases inthe efficacy of the patterns while reductions in the range of scoresdecreases the variance in efficacy across states. FIG. 7C includesexamples of the top five performing 1000 ms long temporal patterns ofSCS evolving between the 1^(st) and 25^(th) generation of the geneticalgorithm. An optimization algorithm typically runs for at least 25generations and may run longer.

FIGS. 8A-8C show the performance of the best pattern across ninedifferent network states and fifty generations with different relativeweightings of the fitness function than in FIGS. 7A-7C. In particular,FIG. 8A includes a representation of multiple network states, while FIG.8B includes a representative graph (boxplots) showing the range ofperformance scores across network states for the best temporal patternof SCS as the algorithm evolves. Decreasing scores as indicated by theplacement of the bar along the y-axis in the plot represent increases inthe efficacy of the patterns while reductions in the range of scoresdecreases the variance in efficacy across states. FIG. 7C includesexamples of the patterns for the 1st and 25th generation of the geneticalgorithm. FIG. 8C includes examples of the top five performing 1000 mslong temporal patterns of SCS evolving between the 1^(st) and 50^(th)generation of the genetic algorithm. An optimization algorithm typicallyruns for at least 25 generations and may run longer.

It is understood that the foregoing detailed description andaccompanying examples are merely illustrative and are not to be taken aslimitations upon the scope of the disclosure, which is defined solely bythe appended claims and their equivalents.

Various changes and modifications to the disclosed embodiments will beapparent to those skilled in the art. Such changes and modifications,including without limitation those relating to the chemical structures,substituents, derivatives, intermediates, syntheses, compositions,formulations, or methods of use of the disclosure, may be made withoutdeparting from the spirit and scope thereof.

For reasons of completeness, various aspects of the disclosure are setout in the following numbered clauses:

Clause 1. A method of identifying an optimized spinal cord simulation(SCS) pattern for pain reduction, the method comprising: generating aplurality of SCS patterns using an optimization algorithm based onpredetermined performance criteria; evaluating the plurality of SCSpatterns for pain reduction using a computational model of a neuronalnetwork; and identifying at least one candidate SCS pattern having anoptimized temporal pattern of stimulation capable of reducing pain.

Clause 2. The method according to clause 1, wherein the plurality of SCSpatterns are generated for at least one of efficiency optimization,efficacy optimization, and variance optimization.

Clause 3. The method according to clause 1 or clause 2, wherein theoptimized SCS pattern reduces pain in a plurality of subjects withdifferent pain states.

Clause 4. The method according to any of clauses 1 to 3, wherein theoptimized SCS pattern comprises a temporal pattern of electricalstimulation pulses.

Clause 5. The method according to clause 4, wherein the temporal patternof electrical stimulation pulses comprises a non-regular temporalpattern with one or more varying inter-pulse intervals.

Clause 6. The method according to clause 4, wherein the optimized SCSpattern comprises a stimulation frequency ranging from about 1 Hz toabout 200 Hz.

Clause 7. The method according to any of clauses 1 to 6, wherein theoptimization algorithm comprises at least one of a genetic algorithm, aparticle swarm algorithm, a simulated annealing algorithm, an ant colonyalgorithm, an estimation of distribution algorithm, a gradient descentalgorithm, and any combinations and derivations thereof.

Clause 8. The method according to any of clauses 1 to 7, wherein thepredetermined performance criteria are incorporated into a fitnessfunction used to evaluate the fitness of the plurality of SCS patterns.

Clause 9. The method according to any of clauses 1 to 7, wherein thepredetermined performance criteria comprise at least one of: i)reduction in pain score; ii) SCS pattern efficiency; and iii) varianceof pain score reduction across different pain states.

Clause 10. The method according to clause 9, wherein the reduction inthe pain score comprises a change in firing rate and/or firing patternof one or more neurons in the computational model.

Clause 11. The method according to clause 9, wherein the SCS patternefficiency is proportional to the average frequency of stimulation.

Clause 12. The method according to clause 9, wherein the variance ofpain score reduction across different pain states corresponds tovariance of the response to SCS across a population of computationalmodels of a neuronal network.

Clause 13. The method according to clause 12, wherein the response toSCS comprises a change in firing rate and/or firing pattern of one ormore neurons in the computational model.

Clause 14. The method according to any of clauses 1 to 13, wherein thecomputational model of the neuronal network is coupled to theoptimization algorithm by the predetermined performance criteria.

Clause 15. The method according to any of clauses 1 to 14, wherein thecomputational model of the neuronal network simulates activity of a widedynamic range (WDR) neuron.

Clause 16. The method according to clause 15, wherein the activity ofthe WDR neuron in the computational model is a proxy for pain.

Clause 17. The method according to any of clauses 1 to 16, wherein thecomputational model of the neural network comprises three network zonescomprising heterogeneous inhibitory and excitatory neural connections.

Clause 18. The method according to any of clauses 1 to 17, wherein thecomputational model of the neural network simulates a network state byvarying at least one of: a biophysical input parameter, a stimulationinput parameter, and a mechanism input parameter.

Clause 19. The method according to clause 18, wherein the computationalmodel simulates a pain state of a subject by varying the at least onebiophysical input parameter, and wherein the at least one biophysicalinput parameter comprises: i) reversal potential of inhibitory synapseswithin each network zone; ii) maximum conductance of GABAergic synapseswithin each network zone; iii) maximum conductance of AMPA synapses ontoinhibitory neurons within each network zone; and iv) number of C fibersactivated in each surround zone.

Clause 20. The method according to clause 19, wherein the reversalpotential of inhibitory synapses ranges from about −50 mV to about −100mV.

Clause 21. The method according to clause 19, wherein the maximumconductance of GABAergic synapses ranges from about 50% to about 100%.

Clause 22. The method according to clause 19, wherein the maximumconductance of AMPA synapses onto inhibitory neurons ranges from about50% to about 100%.

Clause 23. The method according to clause 19, wherein the number of Cfibers activated in each surround zone ranges from about 0% to about50%.

Clause 24. The method according to clause 18, wherein the computationalmodel simulates a response to an SCS pattern by varying the at least onestimulation input parameter, and wherein the at least one stimulationinput parameter comprises: i) number of fibers activated within eachnetwork zone by an SCS pattern; and ii) stimulation frequency of an SCSpattern within each network zone.

Clause 25. The method according to clause 24, wherein the number offibers activated within each network zone by the SCS pattern ranges fromabout 0% to about 100% in a first network zone, from about 0% to about100% in a second network zone, and from about 0% to about 100% in athird network zone.

Clause 26. The method according to clause 24, wherein the stimulationfrequency within each network zone ranges from about 1 Hz to about 200Hz.

Clause 27. The method according to clause 18, wherein the computationalmodel simulates a response to an SCS pattern by varying the at least onemechanism input parameter, and wherein the at least one mechanism inputparameter comprises: i) maximum sodium conductance; and ii) maximumpotassium conductance.

Clause 28. The method according to clause 27, wherein the maximum sodiumconductance ranges from about 50% to about 150% within a network zone.

Clause 29. The method according to clause 27, wherein the maximumpotassium conductance ranges from about 50% to about 150% within anetwork zone.

Clause 30. A system for delivering spinal cord stimulation (SCS) toreduce pain, the system comprising: an electrode sized and configuredfor implantation in proximity to neural tissue; and a pulse generatorcoupled to the electrode, the pulse generator including a power sourcecomprising a battery and a microprocessor coupled to the battery,wherein the pulse generator is configured to generate electrical signalsfor delivering an SCS pattern having an optimized temporal pattern ofelectrical stimulation capable of reducing pain.

Clause 31. The system according to clause 30, wherein the optimized SCSpattern reduces pain in a plurality of subjects with different painstates.

Clause 32. The system according to clause 31 or clause 32, wherein theoptimized SCS pattern comprises non-regular temporal patterns with oneor more varying inter-pulse intervals.

Clause 33. The system according to any of clauses 30 to 32, whereindelivering an SCS pattern having an optimized temporal pattern ofelectrical stimulation comprises delivering one or more SCS patterns toone or more electrodes.

Clause 34. A method for delivering spinal cord stimulation (SCS) toreduce pain using the system of clause 26, the method comprising:programming the pulse generator to output the optimized SCS pattern; anddelivering the SCS pattern to a subject to reduce pain.

Clause 35. The method according to clause 34, wherein the optimized SCSpattern reduces pain in a plurality of subjects with different painstates.

Clause 36. The method according to clause 34 or clause 35, wherein theoptimized SCS pattern comprises non-regular temporal patterns with oneor more varying inter-pulse intervals.

Clause 37. The method according to any of clauses 34 to 36, whereindelivering an optimized SCS pattern to a subject comprises deliveringone or more different SCS patterns to one or more different neuronalpopulations.

What is claimed is:
 1. A method of identifying an optimized spinal cordsimulation (SCS) pattern for pain reduction, the method comprising:generating a plurality of SCS patterns using an optimization algorithmbased on predetermined performance criteria; evaluating the plurality ofSCS patterns for pain reduction using a computational model of aneuronal network; and identifying at least one candidate SCS patternhaving an optimized temporal pattern of stimulation capable of reducingpain.
 2. The method of claim 1, wherein the plurality of SCS patternsare generated for at least one of efficiency optimization, efficacyoptimization, and variance optimization.
 3. The method of claim 1,wherein the optimized SCS pattern reduces pain in a plurality ofsubjects with different pain states.
 4. The method of claim 1, whereinthe optimized SCS pattern comprises a temporal pattern of electricalstimulation pulses.
 5. The method of claim 4, wherein the temporalpattern of electrical stimulation pulses comprises a non-regulartemporal pattern with one or more varying inter-pulse intervals.
 6. Themethod of claim 4, wherein the optimized SCS pattern comprises astimulation frequency ranging from about 1 Hz to about 200 Hz.
 7. Themethod of claim 1, wherein the optimization algorithm comprises at leastone of a genetic algorithm, a particle swarm algorithm, a simulatedannealing algorithm, an ant colony algorithm, an estimation ofdistribution algorithm, a gradient descent algorithm, and anycombinations and derivations thereof.
 8. The method of claim 1, whereinthe predetermined performance criteria are incorporated into a fitnessfunction used to evaluate the fitness of the plurality of SCS patterns.9. The method of claim 1, wherein the predetermined performance criteriacomprise at least one of: i) reduction in pain score; ii) SCS patternefficiency; and iii) variance of pain score reduction across differentpain states.
 10. The method of claim 9, wherein the reduction in thepain score comprises a change in firing rate and/or firing pattern ofone or more neurons in the computational model.
 11. The method of claim9, wherein the SCS pattern efficiency is proportional to the averagefrequency of stimulation.
 12. The method of claim 9, wherein thevariance of pain score reduction across different pain statescorresponds to variance of the response to SCS across a population ofcomputational models of a neuronal network.
 13. The method of claim 12,wherein the response to SCS comprises a change in firing rate and/orfiring pattern of one or more neurons in the computational model. 14.The method of claim 1, wherein the computational model of the neuronalnetwork is coupled to the optimization algorithm by the predeterminedperformance criteria.
 15. The method of claim 1, wherein thecomputational model of the neuronal network simulates activity of a widedynamic range (WDR) neuron.
 16. The method of claim 15, wherein theactivity of the WDR neuron in the computational model is a proxy forpain.
 17. The method of claim 1, wherein the computational model of theneural network comprises three network zones comprising heterogeneousinhibitory and excitatory neural connections.
 18. The method of claim 1,wherein the computational model of the neural network simulates anetwork state by varying at least one of: a biophysical input parameter,a stimulation input parameter, and a mechanism input parameter.
 19. Themethod of claim 18, wherein the computational model simulates a painstate of a subject by varying the at least one biophysical inputparameter, and wherein the at least one biophysical input parametercomprises: i) reversal potential of inhibitory synapses within eachnetwork zone; ii) maximum conductance of GABAergic synapses within eachnetwork zone; iii) maximum conductance of AMPA synapses onto inhibitoryneurons within each network zone; and iv) number of C fibers activatedin each surround zone.
 20. The method of claim 19, wherein the reversalpotential of inhibitory synapses ranges from about −50 mV to about −100mV.
 21. The method of claim 19, wherein the maximum conductance ofGABAergic synapses ranges from about 50% to about 100%.
 22. The methodof claim 19, wherein the maximum conductance of AMPA synapses ontoinhibitory neurons ranges from about 50% to about 100%.
 23. The methodof claim 19, wherein the number of C fibers activated in each surroundzone ranges from about 0% to about 50%.
 24. The method of claim 18,wherein the computational model simulates a response to an SCS patternby varying the at least one stimulation input parameter, and wherein theat least one stimulation input parameter comprises: i) number of fibersactivated within each network zone by an SCS pattern; and ii)stimulation frequency of an SCS pattern within each network zone. 25.The method of claim 24, wherein the number of fibers activated withineach network zone by the SCS pattern ranges from about 0% to about 100%in a first network zone, from about 0% to about 100% in a second networkzone, and from about 0% to about 100% in a third network zone.
 26. Themethod of claim 24, wherein the stimulation frequency within eachnetwork zone ranges from about 1 Hz to about 200 Hz.
 27. The method ofclaim 18, wherein the computational model simulates a response to an SCSpattern by varying the at least one mechanism input parameter, andwherein the at least one mechanism input parameter comprises: i) maximumsodium conductance; and ii) maximum potassium conductance.
 28. Themethod of claim 27, wherein the maximum sodium conductance ranges fromabout 50% to about 150% within a network zone.
 29. The method of claim27, wherein the maximum potassium conductance ranges from about 50% toabout 150% within a network zone.
 30. A system for delivering spinalcord stimulation (SCS) to reduce pain, the system comprising: anelectrode sized and configured for implantation in proximity to neuraltissue; and a pulse generator coupled to the electrode, the pulsegenerator including a power source comprising a battery and amicroprocessor coupled to the battery, wherein the pulse generator isconfigured to generate electrical signals for delivering an SCS patternhaving an optimized temporal pattern of electrical stimulation capableof reducing pain.
 31. The system of claim 30, wherein the optimized SCSpattern reduces pain in a plurality of subjects with different painstates.
 32. The system of claim 30, wherein the optimized SCS patterncomprises non-regular temporal patterns with one or more varyinginter-pulse intervals.
 33. The system of claim 30, wherein delivering anSCS pattern having an optimized temporal pattern of electricalstimulation comprises delivering one or more SCS patterns to one or moreelectrodes.
 34. A method for delivering spinal cord stimulation (SCS) toreduce pain using the system of claim 26, the method comprising:programming the pulse generator to output the optimized SCS pattern; anddelivering the SCS pattern to a subject to reduce pain.
 35. The methodof claim 34, wherein the optimized SCS pattern reduces pain in aplurality of subjects with different pain states.
 36. The method ofclaim 34, wherein the optimized SCS pattern comprises non-regulartemporal patterns with one or more varying inter-pulse intervals. 37.The method of claim 34, wherein delivering an optimized SCS pattern to asubject comprises delivering one or more different SCS patterns to oneor more different neuronal populations.